Building a Common Data Language
Posted: Wed Feb 12, 2025 8:29 am
Use analytics to determine which cloud storage solutions best meet the needs of your various data assets today and down the road. Ideally, you can use analytics and policy-driven automation to move data at the right time to the best storage tier –without excessive storage licensing, egress, and other penalties. This approach will save you the most in the long term while also satisfying the diverse needs of users and meeting corporate requirements.
How a Semantic Layer Drives Collaboration and Trust
The self-service analytics revolution was intended to iran whatsapp number data free business users from IT chains. The idea was to break the IT bottleneck by allowing businesses to create their own data products. After all, businesses understand their respective domains better than anyone in IT could, and decentralizing the work of creating data products should theoretically increase the throughput for developing these products. This decentralized approach failed to deliver on the promise, though. Instead, self-service meant business users needed to become data processing experts. At the same time, embedding semantics into various analytics tools without a common language or standards created inconsistencies in business metrics, destroying trust in their data products.
In this blog post, I will explore how a common semantic modeling language can facilitate collaboration across business teams while drastically improving data accuracy and trust to deliver on the original goal of self-service analytics.
How a Semantic Layer Drives Collaboration and Trust
The self-service analytics revolution was intended to iran whatsapp number data free business users from IT chains. The idea was to break the IT bottleneck by allowing businesses to create their own data products. After all, businesses understand their respective domains better than anyone in IT could, and decentralizing the work of creating data products should theoretically increase the throughput for developing these products. This decentralized approach failed to deliver on the promise, though. Instead, self-service meant business users needed to become data processing experts. At the same time, embedding semantics into various analytics tools without a common language or standards created inconsistencies in business metrics, destroying trust in their data products.
In this blog post, I will explore how a common semantic modeling language can facilitate collaboration across business teams while drastically improving data accuracy and trust to deliver on the original goal of self-service analytics.